Authors:

Hythem Sidky(Chemical and Biomolecular Engineering, University of Notre Dame)

Jonathan Whitmer(Chemical and Biomolecular Engineering, University of Notre Dame)

Juan De Pablo(Institute for Molecular Engineering, Univ of Chicago)

We present an advanced sampling method that efficiently explores phase-space by directly estimating the derivatives of the free energy, also known as the generalized forces, with the aid of a self-regularizing artificial neural network (ANN). The inclusion of an ANN allows a continuous function as an instantaneous estimate of the current free-energy landscape which extends beyond explored regions, providing a significant speed-up over other algorithms that do not generate estimates for unexplored regions. Furthermore, due to the self-regularization scheme, even early, noisy estimates generate accurate, smooth estimates in earlier timepoints. Results from the method are highly transferable, and can be used to expedite sampling in costlier systems by providing estimates from cheaper ones, e.g. a classical molecular dynamics run can be used to provide a starting point for an ab-initio molecular dynamics run, significantly accelerating convergence. This method will be available as an open-source algorithm as a part of Software Suite for Advanced Generalized Ensemble Simulations (SSAGES).

*This project is supported by Midwest Integrated Center for Computational Materials (MICCoM). MICCoM is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences.

To cite this abstract, use the following reference: http://meetings.aps.org/link/BAPS.2018.MAR.R20.8